Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tubealfred/mcp --skill youtube-audience-researchgit clone --depth 1 https://github.com/tubealfred/mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tubealfred/mcp/youtube-audience-research)<a href="https://agentmods.dev/skills/tubealfred/mcp/youtube-audience-research"><img src="https://agentmods.dev/badge/skills/tubealfred/mcp/youtube-audience-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tubealfred/mcp/youtube-audience-research"><img src="https://agentmods.dev/badge/skills/tubealfred/mcp/youtube-audience-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.00507 |
| Opus 5 | $0.00023 | $0.00253 |
| Sonnet 5 | $0.00009 | $0.00101 |
| Haiku 4.5 | $0.00005 | $0.00051 |
Grade A, and why
youtube-audience-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube audience research with TubeAlfred
Connect to the read-only TubeAlfred MCP server at https://mcp.tubealfred.com/. The workflow reads public YouTube data; it cannot identify anonymous viewers, access private Studio analytics, or take actions on a channel.
Start from the user's scope
- For one video, resolve the URL when needed, then use
youtube_comments_list. - For a specific comment thread, use
youtube_comment_repliesonly when replies matter to the question. - For a channel-wide study, use
youtube_channel_getandyoutube_channel_videosto establish the video set, then sample comments only from the videos needed. - Add
youtube_video_transcriptwhen comparing audience language with what the creator actually said.
Pagination rules
Initial comment and reply tools return continuation tokens when more public results exist. Use youtube_comments_page or youtube_comment_replies_page with the matching token. Stop when one of these conditions is met:
- the requested comment count is reached;
- no continuation token remains;
- the evidence is saturated and additional pages no longer change the themes;
- the available credit budget would be exceeded.
Comment results are credit-metered when non-empty. Fetching every page by default is wasteful. Explain sampling limits instead of silently implying complete coverage.
Analysis method
- Preserve direct comment text separately from classifications.
- Group repeated questions, desired outcomes, objections, praise, confusion, and terminology.
- Report counts from the fetched sample, not from the entire audience unless complete coverage was explicitly obtained.
- Include representative quotations only when useful, and avoid exposing unnecessary personal identifiers.
- Compare comments with transcript moments when the user asks what triggered a reaction.
Boundaries
Do not claim sentiment represents all viewers. Do not infer demographics, purchase history, identity, or intent beyond the public text. Authentication, permissions, unavailable data, rate limits, and upstream failures are operational outcomes; buying credits does not resolve them.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 43 lines · 45 tokens per session scan A 6d42bb914ac9
youtube-audience-research is a skill published in the GitHub repository tubealfred/mcp (2 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 507 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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